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A Look Back at 2016’s Top Stories

2017 is already upon us, but it’s worth taking a look back at the past year in addition to forecasting the future. The articles mentioned below were all popular on Electronic Design (albeit biased toward my coverage of the embedded space). The challenge with this type of approach is that articles written recently tend not to show up until next year’s list, like my recent coverage of RISC-V. Still, there are noticeable trends and I have grouped similar articles together. While you may already have seen some, hopefully there are a couple that you may have missed. Rest assured, all are worth a look.

Maker Space and Prototyping IoT

Going Hands-On with the Raspberry Pi 3 (Fig. 1) highlights the importance of the maker space. The Raspberry Pi 3 is the latest in its series and, owing to the large Raspberry Pi community, is especially important. The same holds true for similar platforms like Arduino.

Fig. 11. Raspberry Pi 3 remains one of the top platforms for the maker space.

 

My review of the Raspberry Pi 3 is still valid, but I did find out a couple things with longer-term use of the platform. First, you need to buy an MPEG-2 and/or a VC-1 license to if you plan on use it as a media front end to platforms like MythTV or Kodi. True, it’s only a few extra dollars, but it might be better to buy another platform if that is your end goal. The patents for MPEG-2 will not expire for another year or so.

What’s the Difference Between Consumer and Industrial IoT? (Fig. 2) was high on the list, showing that the Internet of Things is still a hot topic for embedded developers—even with all of the hype and myths associated with it. IoT remains near the top for 2017 predictions and will likely remain the talking point of the decade.

Fig. 22. The Internet of Things remains a hot topic with a simple architecture, but the devil is in the details—especially when it comes to differentiating consumer, commercial and industrial IoT.

 

Not to be outdone, IoT hardware was of interest as well, with Tiny Modules Target IoT Applications (Fig 3). This is just one of many articles on the topic. Tiny, low power, and wireless are key points of interest these days.

Fig. 33. NXP’s SCM-i.MX 6SX incorporates a 1 Ghz Cortex-A9 and an 227 MHz Cortex-M4 in a 13- by 13-mm Package on Package (PoP).

3D printers tend to be commonplace, with low-cost versions running only a couple hundred dollars. There are also high-end specialized printers in production that handle larger items and different materials, from metal to concrete.

NanoDimension’s DragonFly (see the video) targets printed circuit boards. This tends to be an area where there are very few quick prototype alternatives. It can build a multilayer PCB with the same electrical characteristics as a production board made using conventional techniques. This is critical for many applications where tight tolerances are needed to handle features like high-speed serial interfaces.

Embedded Storage

Flash memory has emerged as the top non-volatile storage medium from embedded systems to the enterprise. I think What’s the Difference Between M.2 Modules? (Fig. 4) was a hit because flash storage is taking on so many new roles in new form factors.

Fig. 44. The B and M module can plug into a B or M socket, but a B or M module can only plug into a matching B or M socket.

 

This is also the reason why What’s the Difference Between SD and UHS-II Memory Cards? (Fig. 5) was important.

Fig. 55. The standard SD (far left), Micro SD, SDXC UHS-II, MicroSD UHS-II, and the front view of an SD card (far right) highlight the differences and similarities between the available flash memory options.

 

Check out our What’s the Difference and 11 Myths sections if you like these article formats. They also tend to get lots of comments—take 11 Myths About 8-Bit Microcontrollers, for instance.

Deep Learning

Deep neural networks (DNN) is an aspect of machine learning that has become quite popular for a number of reasons, including hardware to handle like NVidia’s Tesla P100 (Fig. 6). So, Is It Time to Learn About Deep Learning? I say yes.

Fig. 66. Platform like NVidia’s Tesla P100 target deep learning algorithms.

 

The article GPUs and Deep Learning talks about some of the platforms like Tensorflow (Fig. 7) that are available to developers to use and experiment with. Most are open source, and many have a robust and growing community.

Fig. 77. The Tensorflow webpage demonstrates different neural net configurations.

 

Software Highlights

They say that everything old is new again, and technologies that have actually been around a while are just now making their way into the embedded space. What’s the Difference Between SQL, NoSQL, and NewSQL? (Fig. 8) highlights this.

Fig. 88. SQL is a mainstay for embedded developers but other database alternatives are often better for some applications.

 

Finally, a note about Ada. This isn’t a language with a giant community like C or C++, but it is worth a look—if for no other reason than to learn about a new (old) approach to things like Ada or Smalltalk. There are still plenty of myths about Ada, and it is a language that is actually very applicable to embedded systems (even those using microcontrollers).

I suspect that AdaPilot “Sparks” Autopilot Project (Fig. 9) caught many people’s eye more for the autopilot aspect than Ada as a programming language, though it might also have been for the novelty. Still, I remain a staunch Ada advocate after many decades of programming. I still tend to do much more PHP programming than Ada, or even C programming, but I would recommend Ada to anyone doing an embedded project.

Fig. 99. The AdaPilot initial platform NECSarm Computer on Module is based on a STM32F4 ARM Cortex-M processor and supports an isolated CAN interface.

 

So hopefully you found one or two new articles of interest. I hope you like them and stop by to check out the latest articles and trends. 2017 is looking to be a very interesting year from an embedded perspective.

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